

import os
from collections import deque

import torch
from torch.utils.data import Dataset



class        A__(a_							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase=""  ,      _lowercase="train"    )     -> int:
									assert os.path.isdir(_lowercase    )

									a_						:						str 							= []
									a_						:						Optional[Any] 							= os.listdir(_lowercase    )
									for story_filename in story_filenames_list:
												if "summary" in story_filename:
															continue
												a_						:						Dict 							= os.path.join(_lowercase  ,      _lowercase    )
												if not os.path.isfile(_lowercase    ):
															continue
												self.documents.append(_lowercase    )





						def __len__(    self    )     -> Any:
									return len(self.documents    )





						def __getitem__(    self  ,      _lowercase    )     -> Optional[Any]:
									a_						:						Tuple 							= self.documents[idx]
									a_						:						List[str] 							= document_path.split("""/"""    )[-1]
									with open(_lowercase  ,      encoding="""utf-8"""    ) as source:
												a_						:						List[str] 							= source.read()
												a_							,					a_						:						Optional[Any] 							= process_story(_lowercase    )
									return document_name, story_lines, summary_lines







def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Dict 							= list(filter(lambda a__: len(a__) != 0  ,	[line.strip() for line in raw_story.split("""\n""")]))

			# for some unknown reason some lines miss a period, add it
			a_						:						Tuple 							= [_add_missing_period(a__) for line in nonempty_lines]

			# gather article lines
			a_						:						str 							= []
			a_						:						int 							= deque(a__)
			while True:
						try:
									a_						:						Optional[int] 							= lines.popleft()
									if element.startswith("""@highlight"""):
												break
									story_lines.append(a__)
						except IndexError:
									# if "@highlight" is absent from the file we pop
									# all elements until there is None, raising an exception.
									return story_lines, []

    # gather summary lines
			a_						:						Optional[int] 							= list(filter(lambda a__: not t.startswith("""@highlight""")  ,	a__))

			return story_lines, summary_lines


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Dict 							= [""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
			if line.startswith("""@highlight"""):
						return line
			if line[-1] in END_TOKENS:
						return line
			return line + "."


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			if len(a__) > block_size:
						return sequence[:block_size]
			else:
						sequence.extend([pad_token_id] * (block_size - len(a__)))
						return sequence


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= torch.ones_like(a__)
			a_						:						Tuple 							= sequence == pad_token_id
			a_						:						str 							= 0
			return mask


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						Optional[Any] 							= [tokenizer.encode(a__) for line in story_lines]
			a_						:						Optional[int] 							= [token for sentence in story_lines_token_ids for token in sentence]
			a_						:						List[Any] 							= [tokenizer.encode(a__) for line in summary_lines]
			a_						:						str 							= [token for sentence in summary_lines_token_ids for token in sentence]

			return story_token_ids, summary_token_ids



def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						Tuple 							= []
			for sequence in batch:
						a_						:						str 							= -1
						a_						:						str 							= []
						for s in sequence:
									if s == separator_token_id:
												sentence_num += 1
									embeddings.append(sentence_num % 2)
						batch_embeddings.append(a__)
			return torch.tensor(a__)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__snake_case				:      Optional[int]   		=			{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Optional[Any]   		=			[
	    """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLMForMultipleChoice""",
	    """XLMForQuestionAnswering""",
	    """XLMForQuestionAnsweringSimple""",
	    """XLMForSequenceClassification""",
	    """XLMForTokenClassification""",
	    """XLMModel""",
	    """XLMPreTrainedModel""",
	    """XLMWithLMHeadModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Optional[Any]   		=			[
	    """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLMForMultipleChoice""",
	    """TFXLMForQuestionAnsweringSimple""",
	    """TFXLMForSequenceClassification""",
	    """TFXLMForTokenClassification""",
	    """TFXLMMainLayer""",
	    """TFXLMModel""",
	    """TFXLMPreTrainedModel""",
	    """TFXLMWithLMHeadModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
	from .tokenization_xlm import XLMTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm import (
		    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMPreTrainedModel,
		    XLMWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMMainLayer,
		    TFXLMModel,
		    TFXLMPreTrainedModel,
		    TFXLMWithLMHeadModel,
		)

else:
	import sys

	__snake_case				:      Union[str, Any]   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


